Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations7500
Missing cells7580
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory380.9 B

Variable types

Numeric12
Categorical6

Alerts

Annual Income is highly overall correlated with Monthly DebtHigh correlation
Bankruptcies is highly overall correlated with Number of Credit ProblemsHigh correlation
Current Credit Balance is highly overall correlated with Maximum Open Credit and 1 other fieldsHigh correlation
Maximum Open Credit is highly overall correlated with Current Credit BalanceHigh correlation
Monthly Debt is highly overall correlated with Annual Income and 1 other fieldsHigh correlation
Number of Credit Problems is highly overall correlated with BankruptciesHigh correlation
Bankruptcies is highly imbalanced (76.9%) Imbalance
Purpose is highly imbalanced (68.0%) Imbalance
Annual Income has 1557 (20.8%) missing values Missing
Years in current job has 371 (4.9%) missing values Missing
Months since last delinquent has 4081 (54.4%) missing values Missing
Credit Score has 1557 (20.8%) missing values Missing
Maximum Open Credit is highly skewed (γ1 = 74.1942154) Skewed
Id is uniformly distributed Uniform
Id has unique values Unique
Tax Liens has 7366 (98.2%) zeros Zeros
Number of Credit Problems has 6469 (86.3%) zeros Zeros

Reproduction

Analysis started2025-03-11 22:41:32.355505
Analysis finished2025-03-11 22:42:51.108532
Duration1 minute and 18.75 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Uniform  Unique 

Distinct7500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3749.5
Minimum0
Maximum7499
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:51.307645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile374.95
Q11874.75
median3749.5
Q35624.25
95-th percentile7124.05
Maximum7499
Range7499
Interquartile range (IQR)3749.5

Descriptive statistics

Standard deviation2165.2078
Coefficient of variation (CV)0.57746575
Kurtosis-1.2
Mean3749.5
Median Absolute Deviation (MAD)1875
Skewness0
Sum28121250
Variance4688125
MonotonicityStrictly increasing
2025-03-11T22:42:51.695788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
4996 1
 
< 0.1%
5008 1
 
< 0.1%
5007 1
 
< 0.1%
5006 1
 
< 0.1%
5005 1
 
< 0.1%
5004 1
 
< 0.1%
5003 1
 
< 0.1%
5002 1
 
< 0.1%
5001 1
 
< 0.1%
Other values (7490) 7490
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
7499 1
< 0.1%
7498 1
< 0.1%
7497 1
< 0.1%
7496 1
< 0.1%
7495 1
< 0.1%
7494 1
< 0.1%
7493 1
< 0.1%
7492 1
< 0.1%
7491 1
< 0.1%
7490 1
< 0.1%

Home Ownership
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size481.5 KiB
Home Mortgage
3637 
Rent
3204 
Own Home
647 
Have Mortgage
 
12

Length

Max length13
Median length8
Mean length8.7238667
Min length4

Characters and Unicode

Total characters65429
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn Home
2nd rowOwn Home
3rd rowHome Mortgage
4th rowOwn Home
5th rowRent

Common Values

ValueCountFrequency (%)
Home Mortgage 3637
48.5%
Rent 3204
42.7%
Own Home 647
 
8.6%
Have Mortgage 12
 
0.2%

Length

2025-03-11T22:42:52.110496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:42:52.365166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home 4284
36.3%
mortgage 3649
30.9%
rent 3204
27.2%
own 647
 
5.5%
have 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 11149
17.0%
o 7933
12.1%
g 7298
11.2%
t 6853
10.5%
H 4296
 
6.6%
4296
 
6.6%
m 4284
 
6.5%
n 3851
 
5.9%
a 3661
 
5.6%
M 3649
 
5.6%
Other values (5) 8159
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11149
17.0%
o 7933
12.1%
g 7298
11.2%
t 6853
10.5%
H 4296
 
6.6%
4296
 
6.6%
m 4284
 
6.5%
n 3851
 
5.9%
a 3661
 
5.6%
M 3649
 
5.6%
Other values (5) 8159
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11149
17.0%
o 7933
12.1%
g 7298
11.2%
t 6853
10.5%
H 4296
 
6.6%
4296
 
6.6%
m 4284
 
6.5%
n 3851
 
5.9%
a 3661
 
5.6%
M 3649
 
5.6%
Other values (5) 8159
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11149
17.0%
o 7933
12.1%
g 7298
11.2%
t 6853
10.5%
H 4296
 
6.6%
4296
 
6.6%
m 4284
 
6.5%
n 3851
 
5.9%
a 3661
 
5.6%
M 3649
 
5.6%
Other values (5) 8159
12.5%

Annual Income
Real number (ℝ)

High correlation  Missing 

Distinct5478
Distinct (%)92.2%
Missing1557
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean1366391.7
Minimum164597
Maximum10149344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:52.665091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum164597
5-th percentile523393
Q1844341
median1168386
Q31640137
95-th percentile2820297.3
Maximum10149344
Range9984747
Interquartile range (IQR)795796

Descriptive statistics

Standard deviation845339.2
Coefficient of variation (CV)0.61866534
Kurtosis16.742139
Mean1366391.7
Median Absolute Deviation (MAD)380513
Skewness3.0301716
Sum8.120466 × 109
Variance7.1459836 × 1011
MonotonicityNot monotonic
2025-03-11T22:42:53.106784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
969475 4
 
0.1%
1043651 4
 
0.1%
1161660 4
 
0.1%
1058376 4
 
0.1%
1338113 4
 
0.1%
939170 3
 
< 0.1%
1912920 3
 
< 0.1%
1508600 3
 
< 0.1%
1405335 3
 
< 0.1%
2293908 3
 
< 0.1%
Other values (5468) 5908
78.8%
(Missing) 1557
 
20.8%
ValueCountFrequency (%)
164597 1
< 0.1%
175845 1
< 0.1%
177251 1
< 0.1%
191577 1
< 0.1%
192223 1
< 0.1%
194028 1
< 0.1%
199690 1
< 0.1%
201381 1
< 0.1%
206017 1
< 0.1%
216714 1
< 0.1%
ValueCountFrequency (%)
10149344 1
< 0.1%
9338880 1
< 0.1%
8923844 1
< 0.1%
8758449 1
< 0.1%
8710740 1
< 0.1%
8633790 1
< 0.1%
8200229 1
< 0.1%
7999095 1
< 0.1%
7907382 1
< 0.1%
7883442 1
< 0.1%

Years in current job
Categorical

Missing 

Distinct11
Distinct (%)0.2%
Missing371
Missing (%)4.9%
Memory size473.5 KiB
10+ years
2332 
2 years
705 
3 years
620 
< 1 year
563 
5 years
516 
Other values (6)
2393 

Length

Max length9
Median length7
Mean length7.6625053
Min length6

Characters and Unicode

Total characters54626
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row8 years
3rd row6 years
4th row8 years
5th row7 years

Common Values

ValueCountFrequency (%)
10+ years 2332
31.1%
2 years 705
 
9.4%
3 years 620
 
8.3%
< 1 year 563
 
7.5%
5 years 516
 
6.9%
1 year 504
 
6.7%
4 years 469
 
6.3%
6 years 426
 
5.7%
7 years 396
 
5.3%
8 years 339
 
4.5%
(Missing) 371
 
4.9%

Length

2025-03-11T22:42:53.344970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 6062
40.9%
10 2332
 
15.7%
1 1067
 
7.2%
year 1067
 
7.2%
2 705
 
4.8%
3 620
 
4.2%
563
 
3.8%
5 516
 
3.5%
4 469
 
3.2%
6 426
 
2.9%
Other values (3) 994
 
6.7%

Most occurring characters

ValueCountFrequency (%)
7692
14.1%
y 7129
13.1%
e 7129
13.1%
a 7129
13.1%
r 7129
13.1%
s 6062
11.1%
1 3399
6.2%
0 2332
 
4.3%
+ 2332
 
4.3%
2 705
 
1.3%
Other values (8) 3588
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7692
14.1%
y 7129
13.1%
e 7129
13.1%
a 7129
13.1%
r 7129
13.1%
s 6062
11.1%
1 3399
6.2%
0 2332
 
4.3%
+ 2332
 
4.3%
2 705
 
1.3%
Other values (8) 3588
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7692
14.1%
y 7129
13.1%
e 7129
13.1%
a 7129
13.1%
r 7129
13.1%
s 6062
11.1%
1 3399
6.2%
0 2332
 
4.3%
+ 2332
 
4.3%
2 705
 
1.3%
Other values (8) 3588
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7692
14.1%
y 7129
13.1%
e 7129
13.1%
a 7129
13.1%
r 7129
13.1%
s 6062
11.1%
1 3399
6.2%
0 2332
 
4.3%
+ 2332
 
4.3%
2 705
 
1.3%
Other values (8) 3588
6.6%

Tax Liens
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030133333
Minimum0
Maximum7
Zeros7366
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:53.609194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27160354
Coefficient of variation (CV)9.0133919
Kurtosis215.60457
Mean0.030133333
Median Absolute Deviation (MAD)0
Skewness12.993869
Sum226
Variance0.073768485
MonotonicityNot monotonic
2025-03-11T22:42:53.938046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7366
98.2%
1 83
 
1.1%
2 30
 
0.4%
3 10
 
0.1%
4 6
 
0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 7366
98.2%
1 83
 
1.1%
2 30
 
0.4%
3 10
 
0.1%
4 6
 
0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
4 6
 
0.1%
3 10
 
0.1%
2 30
 
0.4%
1 83
 
1.1%
0 7366
98.2%

Number of Open Accounts
Real number (ℝ)

Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.130933
Minimum2
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:54.195776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median10
Q314
95-th percentile20
Maximum43
Range41
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9089241
Coefficient of variation (CV)0.4410164
Kurtosis2.243359
Mean11.130933
Median Absolute Deviation (MAD)3
Skewness1.1176836
Sum83482
Variance24.097536
MonotonicityNot monotonic
2025-03-11T22:42:54.386947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
9 728
 
9.7%
11 692
 
9.2%
10 677
 
9.0%
8 638
 
8.5%
7 613
 
8.2%
12 562
 
7.5%
6 504
 
6.7%
13 465
 
6.2%
14 420
 
5.6%
5 325
 
4.3%
Other values (29) 1876
25.0%
ValueCountFrequency (%)
2 28
 
0.4%
3 95
 
1.3%
4 212
 
2.8%
5 325
4.3%
6 504
6.7%
7 613
8.2%
8 638
8.5%
9 728
9.7%
10 677
9.0%
11 692
9.2%
ValueCountFrequency (%)
43 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
35 1
 
< 0.1%
34 2
 
< 0.1%
33 6
0.1%
32 6
0.1%
31 6
0.1%

Years of Credit History
Real number (ℝ)

Distinct408
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.317467
Minimum4
Maximum57.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:55.082563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q113.5
median17
Q321.8
95-th percentile31.9
Maximum57.7
Range53.7
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation7.0419458
Coefficient of variation (CV)0.38443885
Kurtosis1.6020707
Mean18.317467
Median Absolute Deviation (MAD)4
Skewness1.0466523
Sum137381
Variance49.589
MonotonicityNot monotonic
2025-03-11T22:42:55.311616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 104
 
1.4%
16 99
 
1.3%
16.5 91
 
1.2%
17 86
 
1.1%
17.5 83
 
1.1%
14 82
 
1.1%
18 80
 
1.1%
15.4 72
 
1.0%
18.5 72
 
1.0%
12 70
 
0.9%
Other values (398) 6661
88.8%
ValueCountFrequency (%)
4 1
 
< 0.1%
4.3 1
 
< 0.1%
4.5 2
< 0.1%
4.7 2
< 0.1%
4.8 4
0.1%
4.9 1
 
< 0.1%
5 4
0.1%
5.1 4
0.1%
5.2 2
< 0.1%
5.3 1
 
< 0.1%
ValueCountFrequency (%)
57.7 1
< 0.1%
52.2 1
< 0.1%
51.9 1
< 0.1%
51.5 1
< 0.1%
51.3 1
< 0.1%
51 1
< 0.1%
50.9 1
< 0.1%
50.6 1
< 0.1%
50 1
< 0.1%
49.1 1
< 0.1%

Maximum Open Credit
Real number (ℝ)

High correlation  Skewed 

Distinct6963
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean945153.73
Minimum0
Maximum1.3047262 × 109
Zeros65
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:55.529275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile111604.9
Q1279229.5
median478159
Q3793501.5
95-th percentile1706333.2
Maximum1.3047262 × 109
Range1.3047262 × 109
Interquartile range (IQR)514272

Descriptive statistics

Standard deviation16026217
Coefficient of variation (CV)16.956201
Kurtosis5894.4588
Mean945153.73
Median Absolute Deviation (MAD)236060
Skewness74.194215
Sum7.088653 × 109
Variance2.5683962 × 1014
MonotonicityNot monotonic
2025-03-11T22:42:55.752991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.9%
319110 5
 
0.1%
246224 3
 
< 0.1%
344058 3
 
< 0.1%
513524 3
 
< 0.1%
615714 3
 
< 0.1%
607046 3
 
< 0.1%
358732 3
 
< 0.1%
443630 3
 
< 0.1%
594594 3
 
< 0.1%
Other values (6953) 7406
98.7%
ValueCountFrequency (%)
0 65
0.9%
4334 1
 
< 0.1%
6556 1
 
< 0.1%
6622 1
 
< 0.1%
10890 1
 
< 0.1%
10956 1
 
< 0.1%
11110 1
 
< 0.1%
11132 1
 
< 0.1%
11198 1
 
< 0.1%
12914 1
 
< 0.1%
ValueCountFrequency (%)
1304726170 1
< 0.1%
380052288 1
< 0.1%
265512874 1
< 0.1%
57562560 1
< 0.1%
40923894 1
< 0.1%
26406996 1
< 0.1%
26343328 1
< 0.1%
21372428 1
< 0.1%
19280426 1
< 0.1%
18683808 1
< 0.1%

Number of Credit Problems
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17
Minimum0
Maximum7
Zeros6469
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:55.902858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49859785
Coefficient of variation (CV)2.9329285
Kurtosis32.101329
Mean0.17
Median Absolute Deviation (MAD)0
Skewness4.5642453
Sum1275
Variance0.24859981
MonotonicityNot monotonic
2025-03-11T22:42:56.042085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 6469
86.3%
1 882
 
11.8%
2 93
 
1.2%
3 35
 
0.5%
4 9
 
0.1%
5 7
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 6469
86.3%
1 882
 
11.8%
2 93
 
1.2%
3 35
 
0.5%
4 9
 
0.1%
5 7
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 4
 
0.1%
5 7
 
0.1%
4 9
 
0.1%
3 35
 
0.5%
2 93
 
1.2%
1 882
 
11.8%
0 6469
86.3%

Months since last delinquent
Real number (ℝ)

Missing 

Distinct89
Distinct (%)2.6%
Missing4081
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean34.6926
Minimum0
Maximum118
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:56.229442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median32
Q350
95-th percentile75
Maximum118
Range118
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.688806
Coefficient of variation (CV)0.62517096
Kurtosis-0.78837222
Mean34.6926
Median Absolute Deviation (MAD)16
Skewness0.43132375
Sum118614
Variance470.40431
MonotonicityNot monotonic
2025-03-11T22:42:56.429007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 76
 
1.0%
29 71
 
0.9%
8 68
 
0.9%
33 68
 
0.9%
19 65
 
0.9%
13 65
 
0.9%
12 65
 
0.9%
6 64
 
0.9%
7 64
 
0.9%
10 63
 
0.8%
Other values (79) 2750
36.7%
(Missing) 4081
54.4%
ValueCountFrequency (%)
0 18
 
0.2%
1 26
 
0.3%
2 25
 
0.3%
3 30
0.4%
4 31
0.4%
5 51
0.7%
6 64
0.9%
7 64
0.9%
8 68
0.9%
9 61
0.8%
ValueCountFrequency (%)
118 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
86 1
 
< 0.1%
84 1
 
< 0.1%
83 3
 
< 0.1%
82 4
 
0.1%
81 19
0.3%
80 28
0.4%
79 20
0.3%

Bankruptcies
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing14
Missing (%)0.2%
Memory size439.6 KiB
0.0
6660 
1.0
786 
2.0
 
31
3.0
 
7
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22458
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6660
88.8%
1.0 786
 
10.5%
2.0 31
 
0.4%
3.0 7
 
0.1%
4.0 2
 
< 0.1%
(Missing) 14
 
0.2%

Length

2025-03-11T22:42:56.606868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:42:56.734119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6660
89.0%
1.0 786
 
10.5%
2.0 31
 
0.4%
3.0 7
 
0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14146
63.0%
. 7486
33.3%
1 786
 
3.5%
2 31
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14146
63.0%
. 7486
33.3%
1 786
 
3.5%
2 31
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14146
63.0%
. 7486
33.3%
1 786
 
3.5%
2 31
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14146
63.0%
. 7486
33.3%
1 786
 
3.5%
2 31
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Purpose
Categorical

Imbalance 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size537.6 KiB
debt consolidation
5944 
other
665 
home improvements
 
412
business loan
 
129
buy a car
 
96
Other values (10)
 
254

Length

Max length20
Median length18
Mean length16.3852
Min length5

Characters and Unicode

Total characters122889
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt consolidation
2nd rowdebt consolidation
3rd rowdebt consolidation
4th rowdebt consolidation
5th rowdebt consolidation

Common Values

ValueCountFrequency (%)
debt consolidation 5944
79.3%
other 665
 
8.9%
home improvements 412
 
5.5%
business loan 129
 
1.7%
buy a car 96
 
1.3%
medical bills 71
 
0.9%
major purchase 40
 
0.5%
take a trip 37
 
0.5%
buy house 34
 
0.5%
small business 26
 
0.3%
Other values (5) 46
 
0.6%

Length

2025-03-11T22:42:56.920543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt 5944
41.2%
consolidation 5944
41.2%
other 665
 
4.6%
home 412
 
2.9%
improvements 412
 
2.9%
business 155
 
1.1%
a 133
 
0.9%
buy 130
 
0.9%
loan 129
 
0.9%
car 96
 
0.7%
Other values (15) 414
 
2.9%

Most occurring characters

ValueCountFrequency (%)
o 19553
15.9%
t 13057
10.6%
i 12678
10.3%
n 12642
10.3%
d 11999
9.8%
e 8247
6.7%
s 7012
 
5.7%
6934
 
5.6%
a 6554
 
5.3%
l 6350
 
5.2%
Other values (14) 17863
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 19553
15.9%
t 13057
10.6%
i 12678
10.3%
n 12642
10.3%
d 11999
9.8%
e 8247
6.7%
s 7012
 
5.7%
6934
 
5.6%
a 6554
 
5.3%
l 6350
 
5.2%
Other values (14) 17863
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 19553
15.9%
t 13057
10.6%
i 12678
10.3%
n 12642
10.3%
d 11999
9.8%
e 8247
6.7%
s 7012
 
5.7%
6934
 
5.6%
a 6554
 
5.3%
l 6350
 
5.2%
Other values (14) 17863
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 19553
15.9%
t 13057
10.6%
i 12678
10.3%
n 12642
10.3%
d 11999
9.8%
e 8247
6.7%
s 7012
 
5.7%
6934
 
5.6%
a 6554
 
5.3%
l 6350
 
5.2%
Other values (14) 17863
14.5%

Term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size489.0 KiB
Short Term
5556 
Long Term
1944 

Length

Max length10
Median length10
Mean length9.7408
Min length9

Characters and Unicode

Total characters73056
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShort Term
2nd rowLong Term
3rd rowShort Term
4th rowShort Term
5th rowShort Term

Common Values

ValueCountFrequency (%)
Short Term 5556
74.1%
Long Term 1944
 
25.9%

Length

2025-03-11T22:42:57.087821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:42:57.198628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
term 7500
50.0%
short 5556
37.0%
long 1944
 
13.0%

Most occurring characters

ValueCountFrequency (%)
r 13056
17.9%
o 7500
10.3%
7500
10.3%
T 7500
10.3%
e 7500
10.3%
m 7500
10.3%
S 5556
7.6%
h 5556
7.6%
t 5556
7.6%
L 1944
 
2.7%
Other values (2) 3888
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 13056
17.9%
o 7500
10.3%
7500
10.3%
T 7500
10.3%
e 7500
10.3%
m 7500
10.3%
S 5556
7.6%
h 5556
7.6%
t 5556
7.6%
L 1944
 
2.7%
Other values (2) 3888
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 13056
17.9%
o 7500
10.3%
7500
10.3%
T 7500
10.3%
e 7500
10.3%
m 7500
10.3%
S 5556
7.6%
h 5556
7.6%
t 5556
7.6%
L 1944
 
2.7%
Other values (2) 3888
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 13056
17.9%
o 7500
10.3%
7500
10.3%
T 7500
10.3%
e 7500
10.3%
m 7500
10.3%
S 5556
7.6%
h 5556
7.6%
t 5556
7.6%
L 1944
 
2.7%
Other values (2) 3888
 
5.3%

Current Loan Amount
Real number (ℝ)

Distinct5386
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11873177
Minimum11242
Maximum99999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:57.350678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11242
5-th percentile76186
Q1180169
median309573
Q3519882
95-th percentile99999999
Maximum99999999
Range99988757
Interquartile range (IQR)339713

Descriptive statistics

Standard deviation31926123
Coefficient of variation (CV)2.6889283
Kurtosis3.7548474
Mean11873177
Median Absolute Deviation (MAD)142725
Skewness2.3986592
Sum8.9048831 × 1010
Variance1.0192773 × 1015
MonotonicityNot monotonic
2025-03-11T22:42:57.578176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99999999 870
 
11.6%
218064 6
 
0.1%
89298 6
 
0.1%
222926 5
 
0.1%
265826 5
 
0.1%
223322 5
 
0.1%
216106 5
 
0.1%
270226 5
 
0.1%
111892 4
 
0.1%
267982 4
 
0.1%
Other values (5376) 6585
87.8%
ValueCountFrequency (%)
11242 1
< 0.1%
21472 2
< 0.1%
21516 1
< 0.1%
21560 1
< 0.1%
21582 1
< 0.1%
21604 1
< 0.1%
21626 1
< 0.1%
21714 1
< 0.1%
21736 1
< 0.1%
21780 1
< 0.1%
ValueCountFrequency (%)
99999999 870
11.6%
789030 1
 
< 0.1%
788942 1
 
< 0.1%
788788 1
 
< 0.1%
788634 2
 
< 0.1%
788480 2
 
< 0.1%
788414 1
 
< 0.1%
788018 2
 
< 0.1%
787864 1
 
< 0.1%
787798 1
 
< 0.1%

Current Credit Balance
Real number (ℝ)

High correlation 

Distinct6592
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289833.24
Minimum0
Maximum6506797
Zeros53
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:58.018603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29601.05
Q1114256.5
median209323
Q3360406.25
95-th percentile766593
Maximum6506797
Range6506797
Interquartile range (IQR)246149.75

Descriptive statistics

Standard deviation317871.38
Coefficient of variation (CV)1.0967389
Kurtosis52.946562
Mean289833.24
Median Absolute Deviation (MAD)111682
Skewness5.2013859
Sum2.1737493 × 109
Variance1.0104222 × 1011
MonotonicityNot monotonic
2025-03-11T22:42:58.548620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
 
0.7%
191710 5
 
0.1%
106818 4
 
0.1%
83638 4
 
0.1%
136401 4
 
0.1%
53580 4
 
0.1%
82289 4
 
0.1%
155059 4
 
0.1%
198911 4
 
0.1%
115482 3
 
< 0.1%
Other values (6582) 7411
98.8%
ValueCountFrequency (%)
0 53
0.7%
19 3
 
< 0.1%
57 2
 
< 0.1%
76 2
 
< 0.1%
95 1
 
< 0.1%
114 1
 
< 0.1%
171 1
 
< 0.1%
361 2
 
< 0.1%
456 1
 
< 0.1%
494 1
 
< 0.1%
ValueCountFrequency (%)
6506797 1
< 0.1%
4720132 1
< 0.1%
4367245 1
< 0.1%
4249673 1
< 0.1%
4209659 1
< 0.1%
3944514 1
< 0.1%
3927471 1
< 0.1%
3683340 1
< 0.1%
3547262 1
< 0.1%
3271629 1
< 0.1%

Monthly Debt
Real number (ℝ)

High correlation 

Distinct6716
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18314.454
Minimum0
Maximum136679
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:59.023201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3684
Q110067.5
median16076.5
Q323818
95-th percentile40546.2
Maximum136679
Range136679
Interquartile range (IQR)13750.5

Descriptive statistics

Standard deviation11926.765
Coefficient of variation (CV)0.65122141
Kurtosis5.8328935
Mean18314.454
Median Absolute Deviation (MAD)6611.5
Skewness1.6792352
Sum1.3735841 × 108
Variance1.4224772 × 108
MonotonicityNot monotonic
2025-03-11T22:42:59.473033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.1%
19222 4
 
0.1%
19667 4
 
0.1%
12986 3
 
< 0.1%
13356 3
 
< 0.1%
14683 3
 
< 0.1%
10057 3
 
< 0.1%
11669 3
 
< 0.1%
25511 3
 
< 0.1%
10987 3
 
< 0.1%
Other values (6706) 7465
99.5%
ValueCountFrequency (%)
0 6
0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
42 1
 
< 0.1%
57 1
 
< 0.1%
236 1
 
< 0.1%
280 1
 
< 0.1%
284 2
 
< 0.1%
289 1
 
< 0.1%
373 1
 
< 0.1%
ValueCountFrequency (%)
136679 1
< 0.1%
110311 1
< 0.1%
104036 1
< 0.1%
100091 1
< 0.1%
96177 1
< 0.1%
95508 1
< 0.1%
94674 1
< 0.1%
93640 1
< 0.1%
89789 1
< 0.1%
87994 1
< 0.1%

Credit Score
Real number (ℝ)

Missing 

Distinct268
Distinct (%)4.5%
Missing1557
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean1151.0875
Minimum585
Maximum7510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.7 KiB
2025-03-11T22:42:59.838450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum585
5-th percentile666
Q1711
median731
Q3743
95-th percentile6990
Maximum7510
Range6925
Interquartile range (IQR)32

Descriptive statistics

Standard deviation1604.4514
Coefficient of variation (CV)1.393857
Kurtosis10.063803
Mean1151.0875
Median Absolute Deviation (MAD)14
Skewness3.467217
Sum6840913
Variance2574264.4
MonotonicityNot monotonic
2025-03-11T22:43:00.306783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
740 169
 
2.3%
747 168
 
2.2%
748 157
 
2.1%
745 152
 
2.0%
741 151
 
2.0%
742 151
 
2.0%
743 148
 
2.0%
746 145
 
1.9%
739 144
 
1.9%
738 137
 
1.8%
Other values (258) 4421
58.9%
(Missing) 1557
 
20.8%
ValueCountFrequency (%)
585 1
 
< 0.1%
586 1
 
< 0.1%
588 1
 
< 0.1%
589 1
 
< 0.1%
590 1
 
< 0.1%
591 1
 
< 0.1%
593 1
 
< 0.1%
594 3
< 0.1%
597 2
< 0.1%
598 1
 
< 0.1%
ValueCountFrequency (%)
7510 2
 
< 0.1%
7500 2
 
< 0.1%
7490 2
 
< 0.1%
7480 6
0.1%
7470 4
0.1%
7460 8
0.1%
7450 3
 
< 0.1%
7440 5
0.1%
7430 3
 
< 0.1%
7420 3
 
< 0.1%

Credit Default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size424.9 KiB
0
5387 
1
2113 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5387
71.8%
1 2113
 
28.2%

Length

2025-03-11T22:43:00.806124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T22:43:01.146636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5387
71.8%
1 2113
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 5387
71.8%
1 2113
 
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5387
71.8%
1 2113
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5387
71.8%
1 2113
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5387
71.8%
1 2113
 
28.2%

Interactions

2025-03-11T22:42:46.327424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:41.586212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:48.606089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:56.301751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:04.995195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:13.761338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:19.676500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:26.182194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:29.808931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:32.995437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:37.444185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:42.052774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:46.626541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:42.063169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:49.509309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:56.873085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:06.688343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:14.248122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:20.223928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:26.481285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:30.097172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:30.273218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:39.388880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:09.517294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:16.027385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:27.601492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:34.029902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:39.839370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:43.542644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:48.103247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:44.287795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:52.119977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:58.891364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:09.999186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:16.455264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:23.747571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:27.817205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:31.092117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:34.210825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:40.088486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:43.861581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:48.380266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:44.724899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:52.518555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:41:59.397272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:10.786952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:16.922493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:24.211735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:28.159262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:31.577743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:49.476201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-11T22:42:03.640511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:13.434049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:19.282362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:25.860377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:29.511459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:32.743428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:37.007336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:41.698346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-11T22:42:45.972358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-11T22:43:01.406726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Annual IncomeBankruptciesCredit DefaultCredit ScoreCurrent Credit BalanceCurrent Loan AmountHome OwnershipIdMaximum Open CreditMonthly DebtMonths since last delinquentNumber of Credit ProblemsNumber of Open AccountsPurposeTax LiensTermYears in current jobYears of Credit History
Annual Income1.0000.0160.1030.0410.3960.3970.1490.0080.3940.593-0.102-0.0480.2360.0540.0480.1280.0500.257
Bankruptcies0.0161.0000.0000.0000.0330.0000.0000.0090.0000.0190.0850.6030.0000.0000.0430.0260.0270.041
Credit Default0.1030.0001.0000.4450.0170.2260.0630.0130.0000.0000.0420.0360.0000.0500.0270.1810.0000.031
Credit Score0.0410.0000.4451.0000.007-0.0320.018-0.0080.153-0.062-0.004-0.082-0.0200.035-0.0240.1150.0070.061
Current Credit Balance0.3960.0330.0170.0071.0000.3700.084-0.0170.7730.523-0.014-0.2090.3690.000-0.0250.0510.0340.260
Current Loan Amount0.3970.0000.226-0.0320.3701.0000.009-0.0020.3610.339-0.021-0.0720.1790.0250.0180.0760.0000.137
Home Ownership0.1490.0000.0630.0180.0840.0091.0000.0250.1430.1220.0440.0000.0810.3420.0000.1060.1260.130
Id0.0080.0090.013-0.008-0.017-0.0020.0251.000-0.0190.0030.0090.0080.0070.000-0.0090.0000.007-0.005
Maximum Open Credit0.3940.0000.0000.1530.7730.3610.143-0.0191.0000.427-0.051-0.1860.4740.072-0.0160.0000.0000.287
Monthly Debt0.5930.0190.000-0.0620.5230.3390.1220.0030.4271.000-0.062-0.0580.4550.0390.0140.1540.0440.222
Months since last delinquent-0.1020.0850.042-0.004-0.014-0.0210.0440.009-0.051-0.0621.0000.146-0.0390.0000.0420.0000.030-0.026
Number of Credit Problems-0.0480.6030.036-0.082-0.209-0.0720.0000.008-0.186-0.0580.1461.000-0.0100.0690.3610.0150.0170.090
Number of Open Accounts0.2360.0000.000-0.0200.3690.1790.0810.0070.4740.455-0.039-0.0101.0000.045-0.0060.0690.0200.156
Purpose0.0540.0000.0500.0350.0000.0250.3420.0000.0720.0390.0000.0690.0451.0000.0420.0470.0340.027
Tax Liens0.0480.0430.027-0.024-0.0250.0180.000-0.009-0.0160.0140.0420.361-0.0060.0421.0000.0000.0000.022
Term0.1280.0260.1810.1150.0510.0760.1060.0000.0000.1540.0000.0150.0690.0470.0001.0000.0760.080
Years in current job0.0500.0270.0000.0070.0340.0000.1260.0070.0000.0440.0300.0170.0200.0340.0000.0761.0000.095
Years of Credit History0.2570.0410.0310.0610.2600.1370.130-0.0050.2870.222-0.0260.0900.1560.0270.0220.0800.0951.000

Missing values

2025-03-11T22:42:49.937904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T22:42:50.333785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-11T22:42:50.833650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IdHome OwnershipAnnual IncomeYears in current jobTax LiensNumber of Open AccountsYears of Credit HistoryMaximum Open CreditNumber of Credit ProblemsMonths since last delinquentBankruptciesPurposeTermCurrent Loan AmountCurrent Credit BalanceMonthly DebtCredit ScoreCredit Default
00Own Home482087.0NaN0.011.026.3685960.01.0NaN1.0debt consolidationShort Term99999999.047386.07914.0749.00
11Own Home1025487.010+ years0.015.015.31181730.00.0NaN0.0debt consolidationLong Term264968.0394972.018373.0737.01
22Home Mortgage751412.08 years0.011.035.01182434.00.0NaN0.0debt consolidationShort Term99999999.0308389.013651.0742.00
33Own Home805068.06 years0.08.022.5147400.01.0NaN1.0debt consolidationShort Term121396.095855.011338.0694.00
44Rent776264.08 years0.013.013.6385836.01.0NaN0.0debt consolidationShort Term125840.093309.07180.0719.00
55RentNaN7 years0.012.014.6366784.00.0NaN0.0otherLong Term337304.0165680.018692.0NaN1
66Home Mortgage1511108.010+ years0.09.020.3388124.00.073.00.0home improvementsShort Term99999999.051623.02317.0745.00
77Rent1040060.010+ years0.013.012.0330374.00.018.00.0otherShort Term250888.089015.019761.0705.01
88Home MortgageNaN5 years0.017.015.70.01.0NaN1.0home improvementsShort Term129734.019.017.0NaN0
99Home MortgageNaN1 year0.010.024.6511302.00.06.00.0debt consolidationLong Term572880.0205333.017613.0NaN1
IdHome OwnershipAnnual IncomeYears in current jobTax LiensNumber of Open AccountsYears of Credit HistoryMaximum Open CreditNumber of Credit ProblemsMonths since last delinquentBankruptciesPurposeTermCurrent Loan AmountCurrent Credit BalanceMonthly DebtCredit ScoreCredit Default
74907490Own Home1368000.010+ years0.020.026.7897842.00.069.00.0debt consolidationShort Term683650.0517199.029868.0688.01
74917491Home Mortgage2833185.06 years0.018.021.3280170.00.06.00.0debt consolidationShort Term437404.0108889.061150.0704.00
74927492Home MortgageNaN10+ years0.010.013.3423984.00.0NaN0.0debt consolidationShort Term332948.0161481.020966.0NaN0
74937493Rent1257610.08 years0.014.016.5821480.00.058.00.0debt consolidationLong Term448052.0167428.027562.0676.01
74947494Own HomeNaN< 1 year0.07.08.2301554.00.0NaN0.0debt consolidationShort Term290400.0210938.05070.0NaN0
74957495Rent402192.0< 1 year0.03.08.5107866.00.0NaN0.0otherShort Term129360.073492.01900.0697.00
74967496Home Mortgage1533984.01 year0.010.026.5686312.00.043.00.0debt consolidationLong Term444048.0456399.012783.07410.01
74977497Rent1878910.06 years0.012.032.11778920.00.0NaN0.0buy a carShort Term99999999.0477812.012479.0748.00
74987498Home MortgageNaNNaN0.021.026.51141250.00.0NaN0.0debt consolidationShort Term615274.0476064.037118.0NaN0
74997499RentNaN4 years0.08.09.4480832.00.0NaN0.0debt consolidationShort Term26928.0288192.09061.0NaN0